Every year I take a trip out to Montana to teach at a weekend seminar series that’s part of the University of Montana’s Entertainment Management. I’m 11 years in and I work really hard to create original content for each year. This time around I talked about mental models, theories of communications and information, and a bit about machine learning. I wanted to try to take a bit of the content I shared there and repurpose it. As always, you can subscribe by email here.

Shannon and McLuhan were two of the most important thinkers of the 20th century. Without Shannon we’d have no computers and without McLuhan we wouldn’t examine the effects of media, communications, and technology on society with the urgency we do. With that said, they’re very different in their science and approach. Shannon was fundamentally a mathematician while McLuhan was a scholar of literature. In their work Shannon examined huge questions around how communications works technically, while McLuhan examined how it works tactically. When asked, McLuhan drew the distinction as questions of “transportation” versus “transformation”:

My kind of study of communication is really a study of transformation, whereas Information Theory and all the existing theories of communication I know of are theories of transportation… Information Theory … has nothing to do with the effects these forms have on you… So mine is a transformation theory: how people are changed by the instruments they employ.

I want to take some time to go through both, as they are fascinating in their own ways.

Transportation

Of course, information existed before Shannon, just as objects had inertia before Newton. But before Shannon, there was precious little sense of information as an idea, a measurable quantity, an object fitted out for hard science. Before Shannon, information was a telegram, a photograph, a paragraph, a song. After Shannon, information was entirely abstracted into bits.

The intellectual leaps Shannon made in his paper “A Mathematical Theory of Communications” were miraculous. What starts off as a question about how to reduce noise in the transmission of information turned into a complete theory of information that paved the way for the computing we all rely on. At the base of the whole thing is a recognition that information is probabilistic, which he explains in a kind of beautiful way. Here’s my best attempt to take you through his logic (which some extra explanation from me).

Let’s start by thinking about English for a second. If we wanted to create a list of random letters we could put the numbers 1-27 in a hat (alphabet + space) and pick out numbers one by one and then write down their letter equivalent. When Shannon did this he got:

XFOML RXKHRJFFJUJ ZLPWCFWKCYJ FFJEYVKCQSGHYD QPAAMKBZAACIBZLHJQD

But letters aren’t random at all. If you open a book up and counted all the letters you wouldn’t find 26 letters each occurring 3.8% of the time. On the contrary, letters occur probabilistically “e” occurs more often than “a,” and “a” occurs more often than “g,” which in turn occurs more often than “x.” Put it all together and it looks something like this:

So now imagine we put all our letters (and a space) in a hat. But instead of 1 letter each, we have 100 total tiles in the hat and they alight with the chart above: 13 tiles for “e”, 4 tiles for “d”, 1 tile for “v”. Here’s what Shannon got when he did this:

He called this “first-order approximation” and while it still doesn’t make much sense, it’s a lot less random than the first example.

What’s wrong with that last example is that letters don’t operate independently. Let’s play a game for a second. I’m going to say a letter and you guess the next one. If I say “T” the odds are most of you are going to say “H”. That makes lots of sense since “the” is the most popular word in the English language. So instead of just picking letters at random based on probability what Shannon did next is pick one letter and then match it with it’s probabilistic pair. These are called bigrams and just like we had letter frequencies, we can chart these out.

This time Shannon took a slightly different approach. Rather than loading up a bunch of bigrams in a hat and picking them out at random he turned to a random page in a book and choose a random letter. He then turned to another random page in the same book and found the first occurance of recorded the letter immediately after it. What came out starts to look a lot more like English:

For his “first-order approximation” he picks random words from the book. It looks a lot like a sentence because words don’t occur randomly. There’s a good chance an “and” will come after a word because “and” is likely the third most popular word in the book. Here’s what came out:

REPRESENTING AND SPEEDILY IS AN GOOD APT OR COME CAN DIFFERENT NATURAL HERE HE THE A IN CAME THE TO OF TO EXPERT GRAY COME TO FURNISHES THE LINE MESSAGE HAD BE THESE.

Second-order approximation works just like bigrams, but instead of letters it uses pairs of words.

THE HEAD AND IN FRONTAL ATTACK ON AN ENGLISH WRITER THAT THE CHARACTER OF THIS POINT IS THEREFORE ANOTHER METHOD FOR THE LETTERS THAT THE TIME OF WHO EVER TOLD THE PROBLEM FOR AN UNEXPECTED.

As Shannon put it, “The resemblance to ordinary English text increases quite noticeably at each of the above steps.”

While all that’s cool, much of it was pretty well known at the time. Shannon had worked on cryptography during World War II and used many of these ideas to encrypt/decrypt messages. Where the leap came was how he used this to think about the quantity of information any message contains. He basically realized that the first example, with 27 random symbols (A-Z plus a space), carried with it much more information than his second- or third-order approximation, where subsequent letters were chosen based on their probabilities. That’s because there are fewer “choices” to be made as we introduce bigrams and trigrams, and “choices”, or lack-thereof, are the essence of information.

Until then, communication wasn’t a unified science … There was one medium for voice transmission, another medium for radio, still others for data. Claude showed that all communication was fundamentally the same-and furthermore, that you could take any source and represent it by digital data.

But Shannon didn’t stop there, he goes on to show that all language has redundancy and it can be used to fight noise. The whole thing is pretty mind-blowing and, like I said, underpins all modern computing. (There’s a whole other theory about the relationship between information theory and creativity that I’ll save for another day.)

Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems. The result seems to match the responses you’d expect from a human driver. But what if one day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. And you can’t ask it: there is no obvious way to design such a system so that it could always explain why it did what it did.

Here’s a simple sounding question that has a lot more to it: Why did we evolve the ability to reason? “Objectively, a reasoning mechanism that aims at sounder knowledge and better decisions should focus on reasons why we might be wrong and reasons why other options than our initial hunch might be correct. Such a mechanism should also critically evaluate whether the reasons supporting our initial hunch are strong. But reasoning does the opposite. It mostly looks for reasons that support our initial hunches and deems even weak, superficial reasons to be sufficient.”

A few weeks ago I had a long conversation about the relationship between technology and democracy, the core question being whether or not the two were at odds. I haven’t been able to get it out of my brain since. This piece on weaponized narrative is an interesting addition to the conversation as was this Long Now talk asking “Can Democracy Survive the Internet?” (You can find this on their podcast as well.)

A voter thinking of popping to the polls and then trying out a new pizzeria would be perfectly rational in checking out TripAdvisor, rather than the party manifestos. This is because her vote will almost certainly not make any difference to her life, but her choice of restaurant almost certainly will. We vote because we see it as a civic duty, or a way of being part of something bigger than ourselves. Few people go to the polls under the illusion that they will be casting the deciding vote.

The full explanation for why MIT broke ties with Nectome, a company that promises to store your brain for you (by killing you), is pretty amazing. Here’s a bit to whet your appetite: “Regarding the second point: currently, we cannot directly measure or create consciousness. Given that limitation, how can one say if, for example, a computer or a simulation is conscious?”

At the same time “Brooklyn” has become America’s most significant cultural export. It’s not only 3rd and 4th-tier American cities that adopted the aesthetic. Among many other major international cities, the Shoreditch area of London developed its own version of Contemporary Conformism, as did Daikanyama in Tokyo (where the “Brooklyn” brand possesses cultural cachet as an update to the Americana aesthetic Japanese subcultures have fetishized for 70 years). Of course, America’s other main export during this time was Silicon Valley startup culture and the two found a perfect union and perfect distribution channel in AirBnB and WeWork.